




from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowerCamelCase__            = logging.get_logger(__name__)






class 		_lowerCAmelCase       (							__lowerCAmelCase	):
			"""simple docstring"""
			lowerCAmelCase__           =['''input_features''', '''attention_mask''']
			def __init__(					self     ,		__SCREAMING_SNAKE_CASE=80     ,		__SCREAMING_SNAKE_CASE=1_6000     ,		__SCREAMING_SNAKE_CASE=0.0     ,		__SCREAMING_SNAKE_CASE=10     ,		__SCREAMING_SNAKE_CASE=25     ,		__SCREAMING_SNAKE_CASE="hamming_window"     ,		__SCREAMING_SNAKE_CASE=3_2768.0     ,		__SCREAMING_SNAKE_CASE=0.97     ,		__SCREAMING_SNAKE_CASE=1.0     ,		__SCREAMING_SNAKE_CASE=True     ,		__SCREAMING_SNAKE_CASE=True     ,		__SCREAMING_SNAKE_CASE=False     ,		**__SCREAMING_SNAKE_CASE     ,		)		->							Dict:





				"""simple docstring"""







				super().__init__(feature_size=lowerCamelCase_     ,		sampling_rate=lowerCamelCase_     ,		padding_value=lowerCamelCase_     ,		**lowerCamelCase_       )

				snake_case__						:     Optional[Any]		=feature_size
				snake_case__						:     Union[str, Any]		=sampling_rate
				snake_case__						:     Optional[int]		=padding_value
				snake_case__						:     str		=hop_length
				snake_case__						:     List[Any]		=win_length
				snake_case__						:     List[str]		=frame_signal_scale
				snake_case__						:     str		=preemphasis_coeff
				snake_case__						:     List[str]		=mel_floor
				snake_case__						:     int		=normalize_means
				snake_case__						:     Tuple		=normalize_vars
				snake_case__						:     Any		=win_function
				snake_case__						:     Optional[int]		=return_attention_mask

				snake_case__						:     Tuple		=win_length * sampling_rate // 1000
				snake_case__						:     Union[str, Any]		=hop_length * sampling_rate // 1000

				snake_case__						:     Union[str, Any]		=optimal_fft_length(self.sample_size       )
				snake_case__						:     Union[str, Any]		=(self.n_fft // 2) + 1
			def 					UpperCAmelCase     (					self     ,		__SCREAMING_SNAKE_CASE       )		->							int:





				"""simple docstring"""







				if self.win_function == "hamming_window":
					snake_case__						:     Dict		=window_function(window_length=self.sample_size     ,		name=self.win_function     ,		periodic=lowerCamelCase_       )
				else:
					snake_case__						:     int		=window_function(window_length=self.sample_size     ,		name=self.win_function       )

				snake_case__						:     Any		=mel_filter_bank(
				    num_frequency_bins=self.n_freqs     ,		num_mel_filters=self.feature_size     ,		min_frequency=0.0     ,		max_frequency=self.sampling_rate / 2.0     ,		sampling_rate=self.sampling_rate     ,		)

				snake_case__						:     Optional[Any]		=spectrogram(
				    one_waveform * self.frame_signal_scale     ,		window=lowerCamelCase_     ,		frame_length=self.sample_size     ,		hop_length=self.sample_stride     ,		fft_length=self.n_fft     ,		center=lowerCamelCase_     ,		preemphasis=self.preemphasis_coeff     ,		mel_filters=lowerCamelCase_     ,		mel_floor=self.mel_floor     ,		log_mel='''log'''     ,		)
				return msfc_features.T
			def 					UpperCAmelCase     (					self     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE       )		->							str:





				"""simple docstring"""







				if self.normalize_means:
					snake_case__						:     Any		=x[:input_length].mean(axis=0       )
					snake_case__						:     Union[str, Any]		=np.subtract(lowerCamelCase_     ,		lowerCamelCase_       )
				if self.normalize_vars:
					snake_case__						:     Union[str, Any]		=x[:input_length].std(axis=0       )
					snake_case__						:     List[str]		=np.divide(lowerCamelCase_     ,		lowerCamelCase_       )

				if input_length < x.shape[0]:
					snake_case__						:     str		=padding_value

				# make sure array is in float32
				snake_case__						:     Any		=x.astype(np.floataa       )

				return x
			def 					UpperCAmelCase     (					self     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE = None       )		->							Optional[int]:





				"""simple docstring"""







				snake_case__						:     int		=attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
				return [self._normalize_one(lowerCamelCase_     ,		lowerCamelCase_     ,		self.padding_value       ) for x, n in zip(lowerCamelCase_     ,		lowerCamelCase_       )]


			def __call__(					self     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE = False     ,		__SCREAMING_SNAKE_CASE = None     ,		__SCREAMING_SNAKE_CASE = False     ,		__SCREAMING_SNAKE_CASE = None     ,		__SCREAMING_SNAKE_CASE = None     ,		__SCREAMING_SNAKE_CASE = None     ,		__SCREAMING_SNAKE_CASE = None     ,		**__SCREAMING_SNAKE_CASE     ,		)		->							int:





				"""simple docstring"""







				if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
						raise ValueError(
						    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
						    f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
						    f''' {self.sampling_rate} and not {sampling_rate}.'''       )
				else:
					logger.warning(
					    '''It is strongly recommended to pass the ``sampling_rate`` argument to this function. '''
					    '''Failing to do so can result in silent errors that might be hard to debug.'''       )

				snake_case__						:     Any		=isinstance(lowerCamelCase_     ,		np.ndarray       ) and len(raw_speech.shape       ) > 1
				if is_batched_numpy and len(raw_speech.shape       ) > 2:
					raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''       )
				snake_case__						:     Optional[Any]		=is_batched_numpy or (
				    isinstance(lowerCamelCase_     ,		(list, tuple)       ) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)       ))
				)

				if is_batched:
					snake_case__						:     Any		=[np.asarray(lowerCamelCase_     ,		dtype=np.floataa       ) for speech in raw_speech]
				elif not is_batched and not isinstance(lowerCamelCase_     ,		np.ndarray       ):
					snake_case__						:     Any		=np.asarray(lowerCamelCase_     ,		dtype=np.floataa       )
				elif isinstance(lowerCamelCase_     ,		np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
					snake_case__						:     int		=raw_speech.astype(np.floataa       )

				# always return batch
				if not is_batched:
					snake_case__						:     Optional[int]		=[raw_speech]

				# extract fbank features
				snake_case__						:     Dict		=[self._extract_mfsc_features(lowerCamelCase_       ) for one_waveform in raw_speech]

				# convert into correct format for padding
				snake_case__						:     Optional[Any]		=BatchFeature({'''input_features''': features}       )

				snake_case__						:     Any		=self.pad(
				    lowerCamelCase_     ,		padding=lowerCamelCase_     ,		max_length=lowerCamelCase_     ,		truncation=lowerCamelCase_     ,		pad_to_multiple_of=lowerCamelCase_     ,		return_attention_mask=lowerCamelCase_     ,		**lowerCamelCase_     ,		)
				# make sure list is in array format
				snake_case__						:     Optional[Any]		=padded_inputs.get('''input_features'''       )
				if isinstance(input_features[0]     ,		lowerCamelCase_       ):
					snake_case__						:     Optional[Any]		=[np.asarray(lowerCamelCase_     ,		dtype=np.floataa       ) for feature in input_features]

				snake_case__						:     Any		=padded_inputs.get('''attention_mask'''       )
				if attention_mask is not None:
					snake_case__						:     Tuple		=[np.asarray(lowerCamelCase_     ,		dtype=np.intaa       ) for array in attention_mask]

				if self.normalize_means or self.normalize_vars:
					snake_case__						:     str		=(
					    np.array(lowerCamelCase_     ,		dtype=np.intaa       )
					    if self._get_padding_strategies(lowerCamelCase_     ,		max_length=lowerCamelCase_       ) is not PaddingStrategy.DO_NOT_PAD
					    and padding
					    else None
					)
					snake_case__						:     Tuple		=self.normalize(
					    padded_inputs['''input_features''']     ,		attention_mask=lowerCamelCase_       )

				if return_tensors is not None:
					snake_case__						:     str		=padded_inputs.convert_to_tensors(lowerCamelCase_       )

				return padded_inputs





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


_SCREAMING_SNAKE_CASE 		=      ["""text""", """image""", """audio"""]





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for input_type in input_types:
  if input_type == "text":
   inputs.append("""Text input"""       )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       ).resize((512, 512)       )       )
  elif input_type == "audio":
   inputs.append(torch.ones(3000       )       )
  elif isinstance(UpperCamelCase_							,					UpperCamelCase_       ):
   inputs.append(create_inputs(UpperCamelCase_       )       )
  else:
   raise ValueError(f"""Invalid type requested: {input_type}"""       )

 return inputs





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for output in outputs:
  if isinstance(UpperCamelCase_							,					(str, AgentText)       ):
   output_types.append("""text"""       )
  elif isinstance(UpperCamelCase_							,					(Image.Image, AgentImage)       ):
   output_types.append("""image"""       )
  elif isinstance(UpperCamelCase_							,					(torch.Tensor, AgentAudio)       ):
   output_types.append("""audio"""       )
  else:
   raise ValueError(f"""Invalid output: {output}"""       )

 return output_types


@is_tool_test
class SCREAMING_SNAKE_CASE_		:







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """inputs"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """outputs"""				)				)

    UpperCamelCase			  =      self.tool.inputs
    for _input in inputs:
     if isinstance(_input   ,   lowerCamelCase_				):
      for __input in _input:
       self.assertTrue(__input in authorized_types				)
     else:
      self.assertTrue(_input in authorized_types				)

    UpperCamelCase			  =      self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    # There is a single output
    if len(self.tool.outputs				) == 1:
     UpperCamelCase			  =      [outputs]

    self.assertListEqual(output_types(lowerCamelCase_				)   ,   self.tool.outputs				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """description"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """default_checkpoint"""				)				)
    self.assertTrue(self.tool.description.startswith("""This is a tool that"""				)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)

    for output, output_type in zip(lowerCamelCase_   ,   self.tool.outputs				):
     UpperCamelCase			  =      AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)

    UpperCamelCase			  =      []

    for _input, input_type in zip(lowerCamelCase_   ,   self.tool.inputs				):
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)
